Design and validation of a robust active trailer steering system for multi-trailer articulated heavy vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-trailer articulated heavy vehicles (MTAHVs) are increasingly used around the world due to their economic and environmental benefits. However, MTAHVs exhibit poor maneuverability and low lateral stability, which may lead to fatal traffic accidents. Given the safety risks, it is necessary to solve the steering and stability problems of MTAHVs before they are safely mass deployed on our roads. To this end, active trailer steering (ATS) based on the linear quadratic regulator (LQR) technique has been explored. The LQR-based ATS demonstrates improved maneuverability and enhanced lateral stability. In the ATS design, the vehicle and operating parameters are assumed constant. Thus, it is natural to question the robustness of the ATS in presence of vehicle and operating parameter uncertainties. To address the problem, this paper proposes a robust ATS system. The robust ATS controller is designed using a linear matrix inequality (LMI) based LQR method. In the design, both vehicle and steering actuator parameter uncertainties are considered; to enhance the robustness of the ATS, the weighting matrices of the proposed controller are optimized. The robust controller is applied to an A-Train Double, one type of MTAHV. The effectiveness of the robust ATS is demonstrated using numerical and hardware-in-the-loop real-time simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it